Values

The ad says“Experience and familiarity with agentic AI architectures: multi-step reasoning, tool use, memory, and orchestration.”
The ad says“Proficiency with modern data stack tooling: dbt, Airflow, Spark, or equivalent.”
The ad says“Cloud data platform experience: Snowflake, Azure (preferred), AWS, or GCP.”
The ad says“Cloud data platform experience: Snowflake, Azure (preferred), AWS, or GCP.”
The ad says“Working knowledge of infrastructure-as-code (Terraform, CloudFormation, or equivalent) and containerization (Docker, and ideally Kubernetes or a managed container service).”
The ad says“Proficiency with modern data stack tooling: dbt, Airflow, Spark, or equivalent.”
The ad says“Working knowledge of infrastructure-as-code (Terraform, CloudFormation, or equivalent) and containerization (Docker, and ideally Kubernetes or a managed container service).”
The ad says“Demonstrated LLM experience: prompt engineering, RAG, fine-tuning, or agent frameworks (LangChain, LlamaIndex, or equivalent)”
The ad says“Cloud data platform experience: Snowflake, Azure (preferred), AWS, or GCP.”
The ad says“Working knowledge of infrastructure-as-code (Terraform, CloudFormation, or equivalent) and containerization (Docker, and ideally Kubernetes or a managed container service).”
The ad says“Demonstrated LLM experience: prompt engineering, RAG, fine-tuning, or agent frameworks (LangChain, LlamaIndex, or equivalent)”
The ad says“Demonstrated LLM experience: prompt engineering, RAG, fine-tuning, or agent frameworks (LangChain, LlamaIndex, or equivalent)”
The ad says“Familiarity with ML platform tooling: MLflow, feature stores, model registries, or similar.”
The ad says“Demonstrated LLM experience: prompt engineering, RAG, fine-tuning, or agent frameworks (LangChain, LlamaIndex, or equivalent)”
The ad says“Demonstrated LLM experience: prompt engineering, RAG, fine-tuning, or agent frameworks (LangChain, LlamaIndex, or equivalent)”
The ad says“Cloud data platform experience: Snowflake, Azure (preferred), AWS, or GCP.”
The ad says“Proficiency with modern data stack tooling: dbt, Airflow, Spark, or equivalent.”
The ad says“Working knowledge of infrastructure-as-code (Terraform, CloudFormation, or equivalent) and containerization (Docker, and ideally Kubernetes or a managed container service).”

About Appriss Retail

Appriss Retail is the total retail loss solution for omnichannel, unifying high-quality data across stores, online, and customer ser-vice to reduce returns, cut shrink, and manage incidents. Our products—Engage to reduce returns, Secure to cut shrink, and Incident to centralize visibility—help retailers move from reactive loss control to strategic profit protection. Together, they empower organizations to make better operations decisions, strengthen accountability, and put hundreds of millions back to the bottom line. Covering 40% of all U.S. transactions and active in 45 countries, Appriss Retail is trusted by 60+ of the top 100 U.S. retailers to deliver lasting performance improvement. Learn more at apprissretail.com.

Overview

We are looking for a player-coach who leads a small, high-output data science team while staying deeply hands-on. This role owns the full scope of data science at Appriss Retail — data engineering, governance, and production model delivery — not just model building. The right candidate has built and shipped real data platforms and AI/ML systems using a modern stack, has meaningful experience with LLMs and agentic architectures, and can operate credibly in both the technical weeds and the business conversation.

As a Data Science Manager with 2–4 direct reports from day one, you would set technical direction for the team. This is not a role for someone who manages from a distance. You will write code, review pipelines, define data contracts, and drive architectural decisions — while also growing and directing the team around you.

This is a full-stack role, not a model-building role with a handoff. If your last few projects ended when you handed a notebook or a trained model to a data engineering or ML engineering team to put into production, this probably isn’t the right fit.

Essential Duties

Technical leadership & delivery

Own end-to-end delivery of high-impact data science projects — from ambiguous business request to production-ready system.
Design and maintain data pipelines, data models, and governance standards alongside your team; treat infrastructure as a first-class product concern.
Build, evaluate, and iterate on ML models in production; lead experimentation rigor, monitoring, and lifecycle management.
Architect and ship LLM-integrated features and agentic workflows — including prompt engineering, tool use, and output evaluation.
Guide cloud infrastructure architecture for data science projects, taking into account performance, maintenance, and cost criteria.
Set the standard for code quality: write production-grade Python and SQL, enforce review practices, and maintain documentation.
Partner closely with engineering to integrate models and pipelines into core product infrastructure.

People & team

Directly manage 2–4 data scientists; provide technical mentorship, career development, and clear performance expectations.

Define team operating norms: sprint planning, code review, documentation, and delivery accountability.

Recruit and grow the team as the function scales.

Strategy & stakeholders

Translate ambiguous business problems into well-scoped analytical and modeling work with defined success criteria.

Partner with product, engineering, and business stakeholders to ensure data work is grounded in real source systems and product context — not isolated analysis.

Contribute to the data and analytics roadmap, balancing near-term delivery with longer-term platform investment.

Communicate clearly to non-technical audiences; influence decisions with data and model outputs.

Required Qualifications

Experience

6+ years of experience in data science, data engineering, or a closely related technical discipline.
1+ year of direct people management or formal technical lead experience over a team.

Technical skills — required

Expert-level SQL and Python; production code, not just analysis scripts.

Deep understanding of data infrastructure: pipelines, warehousing, data modeling, and source system behavior.

Strong software engineering practices: version control, code review, testing, and has built or maintained a CI/CD pipeline for a data or ML workload..

Ability to scope and deliver complex analytical projects independently from vague inputs.

Cloud data platform experience: Snowflake, Azure (preferred), AWS, or GCP.
Working knowledge of infrastructure-as-code (Terraform, CloudFormation, or equivalent) and containerization (Docker, and ideally Kubernetes or a managed container service).

Has made and defended a build-vs-buy or cost/latency tradeoff on a production ML or data system.

Familiarity with ML platform tooling: MLflow, feature stores, model registries, or similar.

Required Education

Master's degree or Bachelor's Degree in a technical, quantitative field

Preferred Qualifications

Proficiency with modern data stack tooling: dbt, Airflow, Spark, or equivalent.

Demonstrated LLM experience: prompt engineering, RAG, fine-tuning, or agent frameworks (LangChain, LlamaIndex, or equivalent)

Experience and familiarity with agentic AI architectures: multi-step reasoning, tool use, memory, and orchestration.

Experience in retail, fraud detection, or transaction-level data at scale.

Interview Process / What to Expect

In addition to a technical deep-dive on your modeling skills, the interview process includes a system design conversation where you’ll walk through how you’d architect, deploy, and operate a model or pipeline in production — including infra and cost tradeoffs.

Benefits

At Appriss Retail, we offer a competitive and comprehensive benefits package designed to support your well-being at work and beyond. Benefits begin on your first day and include multiple medical plan options, dental and vision coverage, health savings and flexible spending accounts, paid parental leave, and supplemental coverage for life’s unexpected moments. We offer generous paid time off, a 401(k) with immediate vesting and company match, short- and long-term disability, and free access to health and wellbeing resources such as Calm and Sworkit. You’ll also have access to learning and development opportunities to help you grow your career. Our benefits support your well-being so you can perform your best in every part of life.

Reports to: Director of Data Science

Department: Data Science

Supervisory Duties: Yes, direct management of 2–4 data scientists.

Travel Required: Yes, up to 10% for training and conferences

Location/Work Region: Remote - United States

This job is eligible for a 12-15% bonus in addition to the base salary.

We are proud to be an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to protected characteristics.

Asks for

Values